Object Detection Based on Deep Learning

Abstract

Object detection technology is a crucial research direction in the field of computer vision. Its primary task is to identify and locate objects within images. This technology finds extensive applications across various domains such as autonomous driving, medical diagnostics, and security monitoring. With the development of machine learning, especially the progress of deep learning in related fields of image processing, the accuracy rate of object detection has achieved better results. However, different algorithms have different characteristics, which increases the difficulty of algorithm selection in actual use. This paper starts with the development of object detection, and summarizes three aspects: data set, metrics and object detection algorithm. This paper mainly introduces the commonly used data sets of object detection (such as COCO, VOC), traditional object detection algorithms and deep learning-based object detection algorithms. In addition, the current challenges encountered in object detection, especially in the detection of small objects, and future research directions are discussed.